Volume XLI-B1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 941-946, 2016
https://doi.org/10.5194/isprs-archives-XLI-B1-941-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 941-946, 2016
https://doi.org/10.5194/isprs-archives-XLI-B1-941-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  06 Jun 2016

06 Jun 2016

TOWARD REAL TIME UAVS’ IMAGE MOSAICKING

S. Mehrdad1, M. Satari1, M. Safdary2, and P. Moallem2 S. Mehrdad et al.
  • 1Dept. of Geomatic Engineering ,University of Isfahan, Isfahan, Iran
  • 2Dept. of electrical Engineering ,University of Isfahan, Isfahan, Iran

Keywords: Fast mosaicking, UAVs’ image sequence matching, Fast local descriptor based matching, Epipolar geometry

Abstract. Anyone knows that sudden catastrophes can instantly do great damage. Fast and accurate acquisition of catastrophe information is an essential task for minimize life and property damage. Compared with other ways of catastrophe data acquisition, UAV based platforms can optimize time, cost and accuracy of the data acquisition, as a result UAVs’ data has become the first choice in such condition. In this paper, a novel and fast strategy is proposed for registering and mosaicking of UAVs’ image data. Firstly, imprecise image positions are used to find adjoining frames. Then matching process is done by a novel matching method. With keeping Sift in mind, this fast matching method is introduced, which uses images exposure time geometry, SIFT point detector and rBRIEF descriptor vector in order to match points efficiency, and by efficiency we mean not only time efficiency but also elimination of mismatch points. This method uses each image sequence imprecise attitude in order to use Epipolar geometry to both restricting search space of matching and eliminating mismatch points. In consideration of reaching to images imprecise attitude and positions we calibrated the UAV’s sensors. After matching process, RANSAC is used to eliminate mismatched tie points. In order to obtain final mosaic, image histograms are equalized and a weighted average method is used to image composition in overlapping areas. The total RMSE over all matching points is 1.72 m.